The present invention relates to power management for microgrids and more particularly to estimating battery life in the presence of partial charge and discharge cycles.
Microgrid is an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. Its framework offers a mean to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the grid by generating power close to the consumer. Since microgrid with distributed generation (DG) systems fall within city load centers (<69 kVA) at electric utility substations, near feeders, within neighborhoods, and at industrial, commercial, and residential customer locations [2], storage devices such as battery are necessary to: Manage electric grid peak demand, Improve reliability and outage mitigation, compensate for intermittent power generation from DGs, Provide ancillary services specifically in islanded mode of operation, and Increase electric grid load factor and utilization via the smart grid.
As a result, storage devices are immediate components of microgrids as a mean to achieve high penetration of intermittent renewable energy resources into the grid. The desired size of the energy storage device for distributed energy storage systems (DESS) application is 25-200 kW 1-phase and 25-75 kW 3-phase while its duration and desired lifetime are 2-4 hours and 10-15 years, respectively. The desired values in the case of commercial and industrial (C&I) energy management are 50-1000 kW in size, 3-4 hours duration, and 15 years lifetime [2]. Based on these facts, different battery technologies, such as Lithium-Ion batteries, can be promising candidate for these applications.
The available power from renewable energy components, particularly wind turbine, is highly variable and somewhat random. Consequently, batteries in hybrid power systems, whether in DESS or C&I energy management applications, experience a very irregular pattern of charge and discharge cycles. On the other hand, it is well-known that battery life depends on discharge pattern. Therefore, managing discharge pattern is a promising approach to battery life maximization. Since one can say that maximum battery lifetime can be achieved (i.e. the nominated lifetime) when it always kept idle, there should be a rational definition for maximum lifetime of the battery. In this research, this term is defined as the maximum possible battery lifetime by taking the cost of energy in to consideration at each time step of management. In other words, the maximum battery lifetime is beneficial as long as the cost of the energy provided for the customer is minimum at the time of management.
While storage provides necessary buffer to support the intermittency and unreliability of renewable and other stochastic generation, it is also the most expensive element of the microgrid. This demands real-time power management to guarantee the maximum possible storage lifetime based on the final cost of energy.
In the conventional battery life estimation method it is assumed that life estimation is carried out at the end of each discharge event when depth of discharges DoDs and battery discharge currents for all previous discharge events are known. However in a real-time power management system based on battery life regulation, the life of battery should be estimated at each management time-step even during an ongoing discharge event. This requires to continuously update the information related to the last discharge event in the life estimation block.
Furthermore, the battery cycle life versus depth of discharge DOD data published by the manufactures usually assume that the battery is fully charged before each discharge event. This might not be the case in MicroGrid MG applications where battery experiences many partial charges during its operation. The deteriorating impact of consecutive partial charge cycles on the battery life is well-known but normally not available from battery specification sheets.
Previous attempts have used experimental data to build estimation models which include the impact of partial cycles on battery life.